Data Science and Analytics Certification Course

About Course

Data Science and Analytics with AI Certification Course – Zero to Hero in SQL, Power BI, Python, Machine Learning, Deep Learning, Artificial Intelligence A.I.

Unlock your potential with our comprehensive Data Science and Analytics Certification Program designed to take you from a beginner to an advanced level in key data skills. This hands-on course covers everything you need to know to excel in the field of data science, with a curriculum structured to build a solid foundation and progressively advance to expert-level concepts.

What You’ll Learn:

SQL: Master the art of data extraction, manipulation, and analysis using SQL. Learn how to query databases efficiently and make data-driven decisions.
Power BI: Transform your data into interactive dashboards and reports with Power BI. Discover how to visualize insights in a way that drives business value and storytelling.
Python: Build your data science skillset using Python, the most popular programming language for data analytics. Explore libraries like Pandas, NumPy, and Matplotlib to handle data with ease.
Machine Learning: Dive into the world of predictive analytics by learning Machine Learning algorithms. Understand how to build, train, and evaluate models that can make accurate predictions using real-world data.
Deep Learning: Explore advanced topics in Deep Learning, including neural networks and deep learning frameworks like TensorFlow. Learn how to develop sophisticated models for image recognition, natural language processing, and more.
Artificial Intelligence (AI): Complete your journey by understanding AI’s broader applications and impact. Gain insights into AI techniques and strategies for developing intelligent systems that can solve complex problems.

This course is designed for professionals, students, and enthusiasts who want to become proficient in Data Science and Analytics, no matter their current skill level. With our Zero to Hero approach, you’ll gain hands-on experience, build a strong portfolio, and become job-ready in one of the most in-demand career fields today.

Who Should Enroll:

Aspiring Data Scientists and Analysts
Professionals looking to switch to a career in Data Science
Business Analysts aiming to enhance their data skills
Students and graduates interested in data-driven careers

Join our Data Science and Analytics Certification Program and start your journey to becoming a Data Science professional equipped with industry-relevant skills and real-world problem-solving capabilities.

Gain a solid understanding of databases and data management using SQL.
Write complex queries to extract, filter, and manipulate data efficiently.
Perform data analysis using SQL functions, joins, and aggregations.
Create interactive dashboards and visual reports with Power BI.
Use Power BI features like data modeling, DAX (Data Analysis Expressions), and data visualization tools.
Connect, transform, and visualize data from multiple sources to make data-driven decisions.
Learn the fundamentals of Python programming for data analysis.
Utilize popular libraries like Pandas and NumPy for data manipulation and analysis.
Visualize data using Matplotlib and Seaborn to uncover trends and patterns.
Understand core Machine Learning concepts and techniques.
Build, train, and evaluate machine learning models using real-world data.
Explore advanced Deep Learning techniques, including neural networks and deep learning frameworks like TensorFlow.
Develop sophisticated models for image recognition, natural language processing, and predictive analytics.
Understand the broader applications of Artificial Intelligence (AI) and its impact on industries.
Gain insights into AI techniques for developing intelligent systems and solving complex problems.

Course Content

Welcome to the Course

Welcome in the World of SQL

Basic SQL Queries and Clauses

Installation and Introduction to Azure Data Studio
Creating a Connection to a Database
Company Profile: Adventure Works?
SELECT and FROM Statements
SELECT Specific Columns
Creating a Column Alias
Using WHERE Statement to Filter Rows
Checking the Impact of a WHERE Filter
Using GROUP BY Statement to Combine Rows
Limiting Results to 1 Row for Testing
Using GROUP BY to Combine Rows
Using HAVING Statement to Filter Grouped Rows
Filtering Grouped Rows with HAVING
SQL Order of Operations
Using ORDER BY to Sort Query Rows
Filtering Rows TOP N
Filtering Rows TOP N Percent
Filtering Rows using OFFSET FETCH
Filtering Rows DISTINCT Values

SQL Numeric, String and Logical Functions

Counting Rows with COUNT( ) Aggregation
How Aggregate Functions Respond to NULL Values
The Importance of Data Types
Numeric Data Types
Numeric Functions
Text or String Data Types
String Functions
Comparison Operators
Comparison Operators – Dealing with NULL
Logical Operators
Logical Operators – Common Errors
Advanced Logical Operators – IN and BETWEEN
Advanced Logical Operators – LIKE
Using IIF Statements to Create a Conditional Column
Using a CASE Statement for Multiple Conditions
Basic SQL Formatting
Using IIF in a WHERE Statement
Replacing NULL Using IIF and ISNULL
Using CAST to Change the Data Type

SQL Date Functions

SQL Theory and Data Normalization Concept

Working with Multiple Tables with SQL Joins, UNION and VIEWS

Relationships and ER Diagrams
Purpose of DW Relationships
Types of JOIN
A Basic INNER JOIN Using Sales and Customers
Returning Only the TOP 100 Customers
INNER JOIN the another Table
HAVING or WHERE Statement
When INNER JOIN Doesn’t Work
Is INNER and LEFT Join are same in Industries?
RIGHT JOIN Application
USING Keyword
Appending Data with a UNION
Creating a UNION between Fact Tables
Identifying the Source of Each UNION Row
Using ORDER BY with a UNION
Creating a View
Querying a View

SQL: Complex Queries

Project: World Life Expectancy (Data Cleaning and Exploratory Data Analysis EDA)

Project: Pakistan Household Income (Data Cleaning and Exploratory Data Analysis EDA)

Python: Introduction

Python: Variables and Data Types

Variables
Numbers and Boolean Values in Python
Python Strings
Using Arithmetic Operators in Python
The Double Equality Sign
How to Reassign Values
Add Comments
Understanding Line Continuation
Indexing Elements
Structuring with Indentation
Comparison Operators
Logical and Identity Operators

Python: Conditional Statements

Python: Functions

Python: Sequences

Python: Iterators

Python for Array Manipulation – NumPy

Python for Data Analysis – Pandas (Data Manipulation, Analysis, Cleaning, Transformation and Exploration – EDA)

Introduction to Pandas
Panda Series
DataFrames – Creating a DataFrame
DataFrames – Basic Properties
DataFrames – Working with Columns
DataFrames – Working with Rows
Conditional Filtering
Pandas – Apply on Single Column and Multiple Columns
Statistical Information and Sorting
Missing Data – Overview and Pandas Operations
Group By – Individual vs Multi Index
Combining DataFrames – Concatenation
Combining DataFrames – Inner Merge
Combining DataFrames – Left and Right Merge
Combining DataFrames – Outer Merge
Text Methods for String Data
Time Methods for Date and Time Data
Input and Output – CSV/ HTML/ Excel and SQL Databases
Pandas Pivot Tables

Project: Airbnb Listing Analysis

Python for Data Visualization – Matplotlib (Static Visualizations)

Project 04: World Economic Report

Python for Data Visualization – SeaBorn (Statistical Data Visualization)

Python for Data Visualization – Plotly and Cufflinks (Interactive Visualizations and Dashboards)

Project: Global Video Game Sales Analytics

Data Science in Python: Data Prep & EDA using Pandas, Matplotlib and Seaborn

Project: Bank Customer Data Prep and EDA

Python Advanced: Comprehensions and Sets

Python Advanced: JSON, Generators and Decorators

Python Advanced: APIs

Python Advanced: Logging, Pytest, Pydantic and Databases

Project: Automated Google Sheets Update

Project: Cancer Diagnosis Model

Project: Automated Email System

Introduction of Statistics

Descriptive vs. Inferential Statistics
Measures of Central Tendency: Mean, Median, Mode
Percentile
Analysis: Shoe Sales (Using Mean, Median, Percentile)
Measures of Dispersion: Range, IQR
Box or Whisker Plot
Outlier Treatment Using IQR and Box Plot
Measures of Dispersion: Variance and Standard Deviation
Analysis: Stock Returns Volatility (Using Variance and Std Dev)
Correlation
Correlation vs Causation

Statistics: Probability Theory

Statistics: Distributions

Statistics: Central Limit Theorem

Statistics: Hypothesis Testing

Null vs Alternate Hypothesis
Z Test, Rejection Region
Housing Inflation Test: Rejection Region
p-Value
Housing Inflation Test: p-Value
One-Tailed vs Two-Tailed Test
Type 1 and Type 2 Errors
Statistical Power & Effect Size
A/B Testing
A/B Testing Using Z Test
A/B Testing: Drug Trial

Statistics: Advanced Hypothesis Testing

Introducing Power BI Desktop

Extracting, Transformation and Loading ETL in Power Query

Power BI Front-End vs. Back-End
Types of Data Connectors
Exploring the Power Query Editor
Basic Table Transformations
Storage & Connection Modes
Data Quality Assurance & Profiling Tools
Text Tools
Numerical Tools
Date & Time Tools
Change Type with Locale
Conditional Columns
Grouping & Aggregating Data
Merging Queries
Appending Queries
Appending Files from a Folder
Data Source Settings
Dynamic Data Source using Parameters
Refreshing Queries

Data Modelling and Relationships

Understanding Database Normalization
Explaining Data Normalization (Need) in Excel
Expanded Tables
Context Transition
Evaluation Order
Fact vs Dimension Tables
Primary & Foreign Keys
Relationships vs. Merged Tables
Establishing Table Relationships
Managing & Editing Relationships
Star & Snowflake Schemas
Active & Inactive Relationships
Understanding Relationship Cardinality
Connecting Multiple Fact Tables
Hiding Fields from Report View
Data Formats & Categories
Creating Hierarchies

Calculated Columns with DAX

DAX Measures

Advanced DAX Measures

Time Intelligence Functions

Data Visualization and Dashboard

Cards & Multi-Row Cards Visual
Building & Formatting Charts
Line Chart Visual
Trend Lines & Forecasts
KPI Cards Visual
Report Slicers
Gauge Chart Visual
Advanced Conditional Formatting
Area Chart Visual
Drill Up & Drill Down
Drillthrough Filters
Editing Report Interactions
Adding Bookmarks
Custom Navigation Buttons
Numeric Range and Field Parameters
Custom Tool Tips

Artificial Intelligence AI Visuals

Project: Market Mindz (Marketing Analysis)

Project: CRM Dashboard Analysis

Machine Learning: Introduction

Supervised Machine Learning: Regression

Understanding Ordinary Least Squares
Scikit-Learn Performance Evaluation – Regression
Bias Variance Trade-Off
Polynomial Regression – Choosing Degree of Polynomial
Polynomial Regression – Model Deployment
Regularization Overview
Feature Scaling
Introduction to Cross Validation
Regularization Data Setup
L2 Regularization – Ridge Regression Theory
L2 Regularization – Ridge Regression – Python Implementation
L1 Regularization – Lasso Regression – Background and Implementation
L1 and L2 Regularization – Elastic Net

Supervised Machine Learning: Classification

Introduction to Classification
Logistic Regression: Binary Classification
Model Evaluation: Accuracy, Precision and Recall
Model Evaluation: F1 Score, Confusion Matrix
Logistic Regression: Multiclass Classification
Cost Function: Log Loss
Support Vector Machine (SVM)
Data Pre-processing: Scaling
Sklearn Pipeline
Naive Bayes: Theory
Naive Bayes: SMS Spam Classification
Decision Tree: Theory
Decision Tree: Salary Classification
Handle Class Imbalance: Theory
Handle Class Imbalance Using imblearn: Churn Prediction

KNN – K Nearest Neighbors

Support Vector Machines (SVM)

Decision Trees Learning and Random Forest

Introduction to Tree Based Methods
Decision Tree – History
Decision Tree – Terminology
Decision Tree – Understanding Gini Impurity
Constructing Decision Trees with Gini Impurity
Coding Decision Trees – The Data and Creating the Model
Introduction to Random Forests
Random Forests – History and Motivation
Random Forests – Key Hyperparameters
Random Forests – Number of Estimators and Features in Subsets
Random Forests – Bootstrapping and Out-of-Bag Error
Coding Classification with Random Forest Classifier
Coding Regression with Random Forest Regressor – Data
Coding Regression with Random Forest Regressor – Basic Models
Coding Regression with Random Forest Regressor – Polynomials
Coding Regression with Random Forest Regressor – Advanced Models

Naive Bayes Classification and Natural Language Processing (NLP)

Project: Pokémon Character Identification (Supervised Learning)

Machine Learning – Unsupervised Learning Algorithms

K-Means and Hierarchical Clustering

Introduction to K-Means Clustering Section
Clustering General Overview
K-Means Clustering Theory
K-Means Clustering – Coding
K-Means Color Quantization
K-Means Clustering Exercise Overview and Solutions
Introduction to Hierarchical Clustering
Hierarchical Clustering – Theory and Intuition
Hierarchical Clustering – Coding – Data and Visualization
Hierarchical Clustering – Scikit-Learn

DBSCAN – Density based Spatial Clustering of Applications with Noise

Principal Component Analysis (PCA)

Project: Healthcare Premium Prediction (Regression)

Project: Credit Risk Modelling (Classification)

Project: Mobile Phone Classification Model (Classification)

Project: Credit Card Fraud Detection System

Machine Learning Ops and Model Deployment

What is ML Ops?
Importance of ML Ops in Your Career
ML Flow: Purpose and Overview
ML Flow: Experiment Tracking
ML Flow: Model Registry
ML Flow: Centralized Server Using Dagshub
What is API?
FastAPI Basics
Build FastAPI Server For Credit Risk Project
Git Version Control System
Introduction to ML Cloud Platforms
AWS Sagemaker: Account Setup
AWS Sagemaker: Sagemaker Studio
AWS Sagemaker: 4 Ways to Train Model
AWS Sagemaker: Built In Algorithms
AWS Sagemaker: Script Mode
Data Drift Detection Using PSI & CSI
PSI & CSI: Practical Implementation

Deep Learning: Introduction

Deep Learning: Artificial Neural Networks (ANNs)

What You’ll Need for ANN
Plan of Attack
The Neuron
The Activation Function
How do Neural Networks work?
How do Neural Networks learn?
Gradient Descent
Stochastic Gradient Descent
Backpropagation
Building an Artificial Neural Network (ANN)

Deep Learning: Convolutional Neural Networks (CNNs)

Deep Learning: Recurrent Neural Networks (RNNs)

Deep Learning: Transformers

Project: Weather Prediction Model

Natural Language Processing (NLP): Basics of NLP

Natural Language Processing (NLP): Text Classification

Natural Language Processing (NLP): Semantics and Sentiment Analysis

Natural Language Processing (NLP): AI Chatbot

Project: FoodPanda Product Similarity Matching Model (NLP)

Project: Generative AI Chatbot

Project: Author Identification Model (NLP)

Machine Learning Project with Azure & AWS Deployment

End To End ML Project With Deployment-Github And Code Set Up
Implementing Project Structure, Logging And Exception Handling
Discussing Project Problem Statement, EDA And Model Training
Data Ingestion Implementation
Data Transformation Using Pipelines Implementation
Model Trainer Implementation
Model Hyperparameter Tuning Implementation
Building Prediction Pipeline
ML Project Deployment Using AWS Beanstalk
Deployment EC2 Instance With ECR
Deployment Azure With Container And Images

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About Course

What Will You Learn?

Course Content

Welcome to the Course

Welcome in the World of SQL

What is a Database and it’s need?

Why learning SQL?

Explaining DataLake, DataWarehouse and DataMart

SQL Server vs MySQL vs PostgresSQL

Basic SQL Queries and Clauses

Installation and Introduction to Azure Data Studio

Creating a Connection to a Database

Company Profile: Adventure Works?

SELECT and FROM Statements

SELECT Specific Columns

Creating a Column Alias

Using WHERE Statement to Filter Rows

Checking the Impact of a WHERE Filter

Using GROUP BY Statement to Combine Rows

Limiting Results to 1 Row for Testing

Using GROUP BY to Combine Rows

Using HAVING Statement to Filter Grouped Rows

Filtering Grouped Rows with HAVING

SQL Order of Operations

Using ORDER BY to Sort Query Rows

Filtering Rows TOP N

Filtering Rows TOP N Percent

Filtering Rows using OFFSET FETCH

Filtering Rows DISTINCT Values

SQL Numeric, String and Logical Functions

Counting Rows with COUNT( ) Aggregation

How Aggregate Functions Respond to NULL Values

The Importance of Data Types

Numeric Data Types

Numeric Functions

Text or String Data Types

String Functions

Comparison Operators

Comparison Operators – Dealing with NULL

Logical Operators

Logical Operators – Common Errors

Advanced Logical Operators – IN and BETWEEN

Advanced Logical Operators – LIKE

Using IIF Statements to Create a Conditional Column

Using a CASE Statement for Multiple Conditions

Basic SQL Formatting

Using IIF in a WHERE Statement

Replacing NULL Using IIF and ISNULL

Using CAST to Change the Data Type

SQL Date Functions

Date and Time Data Types

Date Parts

Date and Time Functions

The DATEADD Function

Working with Specific Dates

SQL Theory and Data Normalization Concept

Fact and Dimension Tables

Relationships & Keys

The Star Schema

Snowflake Hybrid Schema

Working with Multiple Tables with SQL Joins, UNION and VIEWS

Relationships and ER Diagrams

Purpose of DW Relationships

Types of JOIN

A Basic INNER JOIN Using Sales and Customers

Returning Only the TOP 100 Customers

INNER JOIN the another Table

HAVING or WHERE Statement

When INNER JOIN Doesn’t Work

Is INNER and LEFT Join are same in Industries?

RIGHT JOIN Application

USING Keyword

Appending Data with a UNION

Creating a UNION between Fact Tables

Identifying the Source of Each UNION Row

Using ORDER BY with a UNION

Creating a View

Querying a View

SQL: Complex Queries

Installing MySQL